PredictionServiceClient

PredictionServiceClient

AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

Constructor

new PredictionServiceClient(optionsopt)

Construct an instance of PredictionServiceClient.

Parameters:
Name Type Attributes Description
options object <optional>

The configuration object. See the subsequent parameters for more details.

Properties
Name Type Attributes Description
credentials object <optional>

Credentials object.

Properties
Name Type Attributes Description
client_email string <optional>
private_key string <optional>
email string <optional>

Account email address. Required when using a .pem or .p12 keyFilename.
keyFilename string <optional>

Full path to the a .json, .pem, or .p12 key downloaded from the Google Developers Console. If you provide a path to a JSON file, the projectId option below is not necessary. NOTE: .pem and .p12 require you to specify options.email as well.
port number <optional>

The port on which to connect to the remote host.
projectId string <optional>

The project ID from the Google Developer's Console, e.g. 'grape-spaceship-123'. We will also check the environment variable GCLOUD_PROJECT for your project ID. If your app is running in an environment which supports Application Default Credentials, your project ID will be detected automatically.
promise function <optional>

Custom promise module to use instead of native Promises.
apiEndpoint string <optional>

The domain name of the API remote host.
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Members

(static) apiEndpoint

The DNS address for this API service - same as servicePath(), exists for compatibility reasons.

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(static) port

The port for this API service.

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(static) scopes

The scopes needed to make gRPC calls for every method defined in this service.

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(static) servicePath

The DNS address for this API service.

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Methods

getProjectId(callback)

Return the project ID used by this class.

Parameters:
Name Type Description
callback function

the callback to be called with the current project Id.
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matchLocationFromModelName(modelName) → {String}

Parse the modelName from a model resource.

Parameters:
Name Type Description
modelName String

A fully-qualified path representing a model resources.
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matchModelFromModelName(modelName) → {String}

Parse the modelName from a model resource.

Parameters:
Name Type Description
modelName String

A fully-qualified path representing a model resources.
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matchProjectFromModelName(modelName) → {String}

Parse the modelName from a model resource.

Parameters:
Name Type Description
modelName String

A fully-qualified path representing a model resources.
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modelPath(project, location, model) → {String}

Return a fully-qualified model resource name string.

Parameters:
Name Type Description
project String
location String
model String
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predict(request, optionsopt, callbackopt) → {Promise}

Perform an online prediction. The prediction result will be directly returned in the response. Available for following ML problems, and their expected request payloads:

  • Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded.
Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
name string

Name of the model requested to serve the prediction.
payload Object

Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.

This object should have the same structure as ExamplePayload
params Object.<string, string> <optional>

Additional domain-specific parameters, any string must be up to 25000 characters long.
options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.
callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing PredictResponse.
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Example
const automl = require('automl.v1');

const client = new automl.v1.PredictionServiceClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const payload = {};
const request = {
  name: formattedName,
  payload: payload,
};
client.predict(request)
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });